Banks are continuously trying to offer their customers additional products. One of the older techniques is to induce customers with savings account to open a certificate of deposit, or to induce customers retaining a certificate of deposit to participate in an investment fund at a higher interest rate. As an inducement to consider the cross sell offer, banks provide everything from a free toaster, to a fee set of Tupperware. The flaw with this technique is that no consideration is given to the customer's financial ability to maintain the cross sell products being offered.
Over the last decade banks have strength their retail cross sell marketing initiative by implementing demographic and lifestyle segmentation systems to better target and match cross sell products. These segmentation systems, breakdown a bank customer database into lifestyle categories, such as elite suburbs, urban core, country families, to rustic living. Each lifestyle category calculates the median age and income of the category population.
When the segment is selected, various statistical models are applied to information stored in these segment classifications to generate a score that reflects the most likely and less likely to be a candidate for a specific bank or retail product. These statistical model ranges from linear and logistic regression to the new decision tree segmentation modeling techniques, like Chi-Square Automatic Interactive Detector (CHAID). The implementation of these classes of technology have contributed to growth in response rate from less than one-half percent a decade ago, to over two percent in today's bank cross sell world.
A deficiency found with these segmentation techniques, is no economic consideration is given to the amount of cash that flows through a customers direct-deposit, time-deposit, and loan accounts to match a customers financial ability to a specific bank initiative. Bank products are still offered as incentives, which in most cases, the bank customer has no interest or financial ability to support the bank product offered.
In the credit card industry world, an economic scoring technique has been development that determines a credit card holders ability to make credit card payments. The FICO scoring algorithm uses a scoring model and mathematical tables to assign points for different pieces of information which best predict future credit card payment behavior.
The FICO scoring algorithm has taught that by measuring and scoring the variation in payment history of credit card customers, that customer future payment ability can be forecast.
Another invention that has taught the use of scoring to target the right bank customer is described in U.S. Pat. No. 6,009,415 issued to Shurling, et al., where each social security number (SSN) stored in a banks customer-information-file is assigned relationship points based on the number bank accounts tied to each SSN and the period each account has been help by the SSN. The length of time that the SSN has been active is considered in the relationship scoring process. Through the computer implementation of the relationship scoring system, the relationship point are summed for each SSN and match to a bank reward, lower interest rates to no service charges, being offered by the bank management for each level of relationship point accumulation.
The deficiency with this invention is that no consideration is given to measuring and scoring the financial information stored in a bank's CIF. The number of bank account and years of longevity misses addressing the financial ability of the SSN holder.
Bank management is continuously pursuing better method to match customers to the right product to cross sell, and the right incentive to reward. With the establishment of the Sarbanes-Oxley Act, the US Patriot Act, and Basel Accord, the forecasting of account performance, the forecasting of default events, the forecasting of account attrition and detection of account fraud has become a major focus of the bank industry. The systems and method available to date have failed to effectively forecast the financial variations of a direct-deposit-account holder to address these demands.